While sparse GPs bring down the cost of inverting the covariance matrix \(\mathbf {K}\)  and predictions with the GP, the number of hyperparameters increases (due to the addition of the inducing points), thereby making inference computationally more expensive. To overcome this, we use variational inference [1]}, which provides a tractable alternative to approximate unknown probability densities in Bayesian models. Below, we briefly provide an overview of sparse GP models and variational inference.
